Automatic Speech Recognition
Transformers
PyTorch
Safetensors
English
whisper
nyansapo_ai-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use eai6/whisper-base.en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eai6/whisper-base.en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="eai6/whisper-base.en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("eai6/whisper-base.en") model = AutoModelForSpeechSeq2Seq.from_pretrained("eai6/whisper-base.en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
whisper-base.en
This model is a fine-tuned version of openai/whisper-base.en on the Azure-dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.0213
- Wer: 19.8990
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0663 | 6.21 | 1000 | 0.0213 | 20.2020 |
| 0.0485 | 12.42 | 2000 | 0.0213 | 19.8990 |
Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for eai6/whisper-base.en
Base model
openai/whisper-base.enEvaluation results
- Wer on Azure-datasettest set self-reported19.899